Development of statistical models for at-site probabilistic seasonal rainfall forecast

Gabriele Villarini, Francesco Serinaldi

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

A probabilistic seasonal rainfall forecasting system for the Bucharest-Filaret (Romania) station based on Generalized Additive Models in Location, Scale and Shape (GAMLSS) is proposed. First we develop statistical models to describe seasonal rainfall over the period 1926-2000, both considering the seasonal record as a continuous time series and accounting for seasonal changes, and by developing ad hoc models for each individual season. The Southern Oscillation Index (SOI), the North Atlantic Oscillation (NAO) and the seasonal rainfall for the previous year are included as possible covariates. Model selection is performed with respect to two penalty criteria [Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC)], each of them leading to different final model configurations in terms of predictors and their functional relation to the parameters of the probability distribution. Retrospective forecast, in which the parameters of the models are re-estimated every time new information becomes available, is performed on a yearly basis for the period 1986-2000. The quality of the forecasts is assessed in terms of several accuracy measures and by visual examination of the forecasts' probability distributions. The best forecasts are obtained for the winter season. While it is not possible to identify a single 'best' model according to all the forecast measures, we recommend using the model that considers the seasonal rainfall as a continuous time series and penalized with respect to AIC.

Original languageEnglish (US)
Pages (from-to)2197-2212
Number of pages16
JournalInternational Journal of Climatology
Volume32
Issue number14
DOIs
StatePublished - Nov 30 2012

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Keywords

  • GAMLSS
  • Rainfall
  • Seasonal forecast

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